Fuzzy clustering of incomplete data based on missing attribute interval size
Title | Fuzzy clustering of incomplete data based on missing attribute interval size |
Publication Type | Conference Paper |
Year of Publication | 2015 |
Authors | Zhang, L., Li, B., Zhang, L., Li, D. |
Conference Name | 2015 IEEE 9th International Conference on Anti-counterfeiting, Security, and Identification (ASID) |
Date Published | Sept. 2015 |
Publisher | IEEE |
ISBN Number | 978-1-4673-7140-7 |
Keywords | Algorithm design and analysis, Breast, Clustering algorithms, data handling, data structures, Fuzzy C-Means, fuzzy c-means algorithm, fuzzy clustering, fuzzy set theory, Incomplete Data, incomplete data clustering, interval data set, interval median, Interval size, Iris, missing attribute interval size, nearest neighbor rule, pattern clustering, Prototypes, pubcrawl170107, similar object cluster identification, Standards, TICI data set |
Abstract | Fuzzy c-means algorithm is used to identity clusters of similar objects within a data set, while it is not directly applied to incomplete data. In this paper, we proposed a novel fuzzy c-means algorithm based on missing attribute interval size for the clustering of incomplete data. In the new algorithm, incomplete data set was transformed to interval data set according to the nearest neighbor rule. The missing attribute value was replaced by the corresponding interval median and the interval size was set as the additional property for the incomplete data to control the effect of interval size in clustering. Experiments on standard UCI data set show that our approach outperforms other clustering methods for incomplete data. |
URL | https://ieeexplore.ieee.org/document/7405670 |
DOI | 10.1109/ICASID.2015.7405670 |
Citation Key | zhang_fuzzy_2015 |
- interval data set
- TICI data set
- standards
- similar object cluster identification
- pubcrawl170107
- Prototypes
- pattern clustering
- nearest neighbor rule
- missing attribute interval size
- Iris
- Interval size
- interval median
- Algorithm design and analysis
- incomplete data clustering
- Incomplete Data
- fuzzy set theory
- fuzzy clustering
- fuzzy c-means algorithm
- Fuzzy C-Means
- data structures
- data handling
- Clustering algorithms
- Breast